A Comprehensive Study on Unsupervised Transfer Learning for Structural Health Monitoring of Bridges Using Joint Distribution Adaptation

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Federal do Pará (UFPA)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorLusófona University-
Autor(es): dc.contributorUniversidade de Lisboa-
Autor(es): dc.creatorSouza, Laura-
Autor(es): dc.creatorYano, Marcus Omori-
Autor(es): dc.creatorda Silva, Samuel-
Autor(es): dc.creatorFigueiredo, Eloi-
Data de aceite: dc.date.accessioned2025-08-21T17:42:06Z-
Data de disponibilização: dc.date.available2025-08-21T17:42:06Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-08-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/infrastructures9080131-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306038-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306038-
Descrição: dc.descriptionBridges are crucial transportation infrastructures with significant socioeconomic impacts, necessitating continuous assessment to ensure safe operation. However, the vast number of bridges and the technical and financial challenges of maintaining permanent monitoring systems in every single bridge make the implementation of structural health monitoring (SHM) difficult for authorities. Unsupervised transfer learning, which reuses experimental or numerical data from well-known bridges to detect damage on other bridges with limited monitoring response data, has emerged as a promising solution. This solution can reduce SHM costs while ensuring the safety of bridges with similar characteristics. This paper investigates the limitations, challenges, and opportunities of unsupervised transfer learning via domain adaptation across datasets from various prestressed concrete bridges under distinct operational and environmental conditions. A feature-based transfer learning approach is proposed, where the joint distribution adaptation method is used for domain adaptation. As the main advantage, this study leverages the generalization of SHM for damage detection in prestressed concrete bridges with limited long-term monitoring data.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionApplied Electromagnetism Laboratory Universidade Federal do Pará, R. Augusto Corrêa, Guamá 01, PA-
Descrição: dc.descriptionDepartamento de Engenharia Mecânica UNESP—Universidade Estadual Paulista, SP-
Descrição: dc.descriptionFaculty of Engineering Lusófona University, Campo Grande 376-
Descrição: dc.descriptionCERIS Instituto Superior Técnico Universidade de Lisboa, Av. Rovisco Pais 1-
Descrição: dc.descriptionDepartamento de Engenharia Mecânica UNESP—Universidade Estadual Paulista, SP-
Descrição: dc.descriptionFAPESP: 24/00720-8-
Idioma: dc.languageen-
Relação: dc.relationInfrastructures-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectbridges-
Palavras-chave: dc.subjectdomain adaptation-
Palavras-chave: dc.subjectjoint distribution adaptation-
Palavras-chave: dc.subjectpattern recognition-
Palavras-chave: dc.subjectstructural health monitoring-
Palavras-chave: dc.subjectunsupervised transfer learning-
Título: dc.titleA Comprehensive Study on Unsupervised Transfer Learning for Structural Health Monitoring of Bridges Using Joint Distribution Adaptation-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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